大型语言模型作为个性化知识 grounded 对话的源规划器
Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogue
AI总结：
提出 SAFARI 框架，利用大型语言模型规划多个知识源以生成个性化知识 grounded 对话，并构建首个考虑人格与隐式知识依赖的 KBP 数据集，实验证明其有效性。
摘要：
开放域对话系统通常需要不同来源的知识来生成更具信息性和证据性的回应。然而，现有的知识 grounded 对话系统要么专注于单一知识源，要么忽略多个知识源之间的依赖关系，这可能导致生成不一致甚至矛盾的回应。为了整合多个知识源及其之间的依赖关系，我们提出了 SAFARI，一种新颖的框架，利用大型语言模型（LLMs）在规划和理解及整合方面的卓越能力，在监督和无监督设置下均适用。具体来说，SAFARI 将知识 grounded 解耦为多个源和回应生成，这使得能够轻松扩展到各种知识源，包括可能不使用任何源的情况。为了研究该问题，我们构建了一个个性化的知识 grounded 对话数据集 \textit{\textbf{K}nowledge \textbf{B}ehind \textbf{P}ersona}~(\textbf{KBP})，这是首个考虑人格与隐式知识之间依赖关系的数据集。在 KBP 数据集上的实验结果表明，SAFARI 框架能够有效生成人格一致且知识增强的回应。
英文摘要：
Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses. However, existing knowledge-grounded dialogue systems either focus on a single knowledge source or overlook the dependency between multiple sources of knowledge, which may result in generating inconsistent or even paradoxical responses. To incorporate multiple knowledge sources and dependencies between them, we propose SAFARI, a novel framework that leverages the exceptional capabilities of large language models (LLMs) in planning, understanding, and incorporating under both supervised and unsupervised settings. Specifically, SAFARI decouples the knowledge grounding into multiple sources and response generation, which allows easy extension to various knowledge sources including the possibility of not using any sources. To study the problem, we construct a personalized knowledge-grounded dialogue dataset \textit{\textbf{K}nowledge \textbf{B}ehind \textbf{P}ersona}~(\textbf{KBP}), which is the first to consider the dependency between persona and implicit knowledge. Experimental results on the KBP dataset demonstrate that the SAFARI framework can effectively produce persona-consistent and knowledge-enhanced responses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
